VisionOPS

About the Project

VisionOPS is a no-code AI computer vision training platform that puts the full power of deep learning into the hands of non-technical users. From raw images and video footage to a production-ready model — everything happens inside a single browser-based interface. Teams can build, train, evaluate, and download object detection and image classification models without installing a single dependency or writing a single line of code.

Dataset Management

• Upload images individually or in bulk — supports JPG, PNG, BMP, and TIFF formats
• Automatic frame extraction from uploaded video files — select FPS rate to control dataset density instantly
• In-browser image editor for cropping, rotating, brightness/contrast adjustments, and resizing before annotation
• Dataset pre-analysis engine: automatically detects class imbalance, duplicate images, low-resolution samples, mislabelled bounding boxes, empty annotation files, and truncated images — with one-click auto-fix for correctable issues
• Improvement recommendations engine: suggests augmentation strategies, minimum sample counts per class, and labelling consistency fixes based on dataset statistics

Annotation

• Built-in bounding box annotation tool with class selector, zoom, and pan — no external labelling tool needed
• Polygon and segmentation mask annotation support for instance segmentation tasks
• Keyboard shortcut-driven labelling workflow for high-speed annotation
• Annotation export in YOLO TXT, COCO JSON, Pascal VOC XML formats
• Auto-annotation assistance: AI pre-labels images using an already-trained model so annotators only review and correct

Model Training

• Full support for modern object detection and instance segmentation models
• Classification model training with leading modern architectures
• Configurable hyperparameters: epochs, batch size, learning rate, image size, augmentation pipeline, optimizer, and early stopping
• One-click training start — model selection, dataset split (train/val/test), and config generation handled automatically
• Training controls: start, stop, pause, and resume — training state is persisted so paused runs can continue without data loss
• Transfer learning from pre-trained COCO weights or custom checkpoint uploads to accelerate convergence on small datasets

Real-time Training Monitoring

• Live training dashboard with real-time charts: loss curves (box loss, object loss, class loss), mAP@0.5, mAP@0.5:0.95, precision, recall, and F1 score — updated every epoch
• Per-class metrics table with individual AP, precision, and recall breakdowns
• Confusion matrix visualisation with interactive class-level drill-down
• GPU utilisation, memory usage, and estimated time-to-completion displayed live
• Training log stream — raw stdout from the training process streamed directly to the browser console panel

Model Comparison & Analysis

• Side-by-side multi-model comparison dashboard — select any two or more trained models and compare mAP, precision, recall, F1, inference speed, and model size
• Inference test panel — upload a test image and run it against any trained model directly in the browser to see predictions with confidence scores and bounding boxes
• Model version history: every training run is saved with its configuration, metrics, and weights for full reproducibility
• One-click model download — export trained weights in common production formats for edge and server deployment

Dataset Versioning & Analysis

• Create multiple independent datasets per project — organise by task type, capture session, or experiment
• Fork any dataset into a new version: add fresh images or re-annotate existing ones without touching the original, preserving full lineage
• Dataset diff view — visual side-by-side comparison of two dataset versions showing exactly which images were added, removed, or re-annotated
• Cross-version training result comparison: train on dataset v1 and v2 side by side and compare how adding 200 new images to a class shifted mAP, precision, recall, and F1
• Dataset statistics dashboard: image count per class, annotation density heatmap, bounding box size distribution, and train/val/test split visualisation
• Merge two datasets into a unified new dataset with automatic deduplication and conflict resolution for overlapping annotations
• Snapshot locking — freeze a dataset version so it cannot be modified, ensuring full reproducibility of any historical training run

Model Management

• Central model registry: every trained model is catalogued with its source dataset version, architecture, hyperparameters, training duration, hardware used, and final metrics
• Model versioning — train the same architecture multiple times across different dataset versions or config changes; all versions are stored, searchable, and independently accessible
• Tag models as Production, Staging, or Archived — instantly identify which version is deployed and which is under evaluation
• Registry search and filter by architecture type, mAP threshold, dataset version, training date range, or custom tags
• Direct model-to-model lineage graph: see which dataset version and base checkpoint each model was derived from
• Bulk actions: delete, archive, or batch-export selected model versions in a single operation
• Download any registered model version in common production formats

Industrial Integration & Enterprise Deployment

• REST API endpoints for every platform action — trigger training, query model status, fetch results, and download weights programmatically from any external system
• Embed trained models directly into existing company AI pipelines via the VisionOPS API without any code changes on the platform side
• Export options ensure models are deployment-ready for edge devices, embedded systems, and industrial cameras
• Webhook notifications — receive real-time alerts in external systems (ERP, MES, SCADA, Slack, Teams) when a training run completes or a dataset version is published
• Role-based access control with team workspaces — different departments or clients can each manage their own datasets and models within a shared organisation account

Sector Coverage

VisionOPS is designed to serve any industry that benefits from visual AI — without requiring in-house data science expertise:

• Manufacturing & Quality Control — surface defect detection, dimensional inspection, assembly verification on production lines
• Agriculture — crop disease detection, weed classification, fruit counting and grading from drone or field camera footage
• Retail & Logistics — shelf stock monitoring, package damage detection, barcode and label reading automation
• Healthcare & Life Sciences — medical image pre-screening, pathology slide classification, equipment monitoring
• Security & Surveillance — person and vehicle detection, restricted area monitoring, PPE compliance verification
• Construction & Infrastructure — crack detection on structures, site safety monitoring, progress tracking from drone imagery
• Energy & Utilities — solar panel fault detection, powerline inspection, meter reading automation

Any team — regardless of technical background — can build, iterate, and deploy production-grade computer vision models tailored to their industry, and integrate the results directly into their existing software ecosystem through the VisionOPS API.

Mobile & Responsive Design

VisionOPS is fully responsive and designed to work across desktop, tablet, and mobile screens. The annotation workspace, training dashboards, real-time metric charts, dataset management panels, and model registry are all optimised for touch-friendly interaction and adaptive layouts — so team members can review training progress, approve dataset changes, and inspect model results from any device, anywhere.